ArviZ 1.0

Refactoring for flexibility, extensibility, and power

Osvaldo Martin

Aalto University

2025-11-11

What is ArviZ?

The bass player of Bayesian modelling (with Python)

Plays a key supporting role:
- Model evaluation
- Model comparison
- MCMC diagnostics
- Presentation of results
- …

The origin story

A PyMC’s spin-off
March 2018 we decide to move out diagnostics and plotting out of PyMC code base

Why a refactoring?

We were never really happy with the design, but people needed the tools!
A few issues:
- dependencies: sometimes NumPy is all you need
- messy arguments: flexibility scaled with number of arguments.
- hard to reuse code: both for us and for users.
- duplicated code: similar functionality implemented twice (matplotlib/bokeh).
- …

Divide and conquer

All ArviZ functionality is available in 3 packages

arviz-base: I/O and datastructure manipulation
arviz-stats: for statistical functions and diagnostics
arviz-plots: visual checks

Note

We expect most users to install everything together, so for them ArviZ is still one package.

ArviZ-base

import arviz_base as azb
dt = azb.load_arviz_data("anes")
dt

ArviZ-stats

import arviz_stats as azs

azs.ess(dt)

ArviZ-stats array-interface

from arviz_stats.base import array_stats

# generate mock MCMC-like data
rng = np.random.default_rng()
samples = rng.normal(size=(4, 1000, 3))

array_stats.ess(samples, chain_axis=0, draw_axis=1)

array([3714.58251871, 3983.81458725, 3999.80437052])

ArviZ-plots

azp.plot_dist(dt, var_names=["~p"]).show()

ArviZ-plots

azp.plot_dist(dt, var_names=["~p"],
              visuals={"dist": {"color": "C5", "linestyle": "C2"},
                       "credible_interval": False,
                       "point_estimate": False,
                       "point_estimate_text": False,
                      }).show()

ArviZ-plots

azp.plot_dist(dt, var_names=["Intercept", "party_id:age"],
    kind = "ecdf",
    visuals ={"point_estimate_text": False},
    cols = ["__variable__"],
    aes = {"color": ["party_id:age_dim"], "y": ["party_id:age_dim"]},
    y = np.linspace(0, 0.1, 4),
    aes_by_visuals={
        "dist": ["color"],
        "point_estimate": ["color", "y"],
        "credible_interval": ["y"]
    },
).show()

Where are we now?

not yet at 1.0, but really close
if you are already using ArviZ you can try

pip install -U arviz
pip install arviz-plots["backend"] 
# backend ∈ {"matplotlib", "plotly", "bokeh"}

and then:

import arviz as az  # old arviz
import arviz.preview as azp  # new arviz